Computer-implemented clustering systems and methods for action determination

ABSTRACT

Computer-implemented systems and methods for determining one or more actions to be taken with respect to a first entity. A computer-implemented method can be configured to receive data that is related to characteristics of the first entity as well as data that is related to a plurality of segments. Assignments are determined between the first entity and the segments based upon the characteristics of the first entity and the characteristics associated with the segments. A determined assignment includes a membership probability that is indicative of how probable is membership of the first entity with respect to a segment. One or more actions are determined for the first entity based upon the membership probabilities and action information associated with the assigned segments.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims priority to and the benefit of U.S. ApplicationSer. No. 60/902,379, (entitled “Computer-Implemented Systems and MethodsFor Action Determination” and filed on Feb. 20, 2007), of which theentire disclosure (including any and all figures) is incorporated hereinby reference.

This application contains subject matter that may be considered relatedto subject matter disclosed in: U.S. Application Ser. No. 60/902,378,(entitled “Computer-Implemented Modeling Systems and Methods foranalyzing Computer Network Intrusions” and filed on Feb. 20, 2007); U.S.Application Ser. No. 60/902,380, (entitled “Computer-ImplementedSemi-supervised Learning Systems And Methods” and filed on Feb. 20,2007); U.S. Application Ser. No. 60/902,381, (entitled“Computer-Implemented Guided Learning Systems and Methods forConstructing Predictive Models” and filed on Feb. 20, 2007); U.S.Application Ser. No. 60/786,039 (entitled “Computer-ImplementedPredictive Model Generation Systems And Methods” and filed on Mar. 24,2006); U.S. Application Ser. No. 60/786,038 (entitled“Computer-Implemented Data Storage For Predictive Model Systems” andfiled on Mar. 24, 2006); and to U.S. Provisional Application Ser. No.60/786,040 (entitled “Computer-Implemented Predictive Model ScoringSystems And Methods” and filed on Mar. 24, 2006); of which the entiredisclosures (including any and all figures) of all of these applicationsare incorporated herein by reference.

TECHNICAL FIELD

This document relates generally to computer-implemented clusteringsystems and more particularly to performing clustering operations fordetermining action(s) to be taken with respect to an entity.

BACKGROUND

The financial industry processes an inordinate number of transactionsfor their current or prospective customers. Many of these transactionsdemand that some action be taken on the part of a financial company inorder to more completely handle a transaction. As an example, anindividual working for a credit card company may be tasked withdetermining which credit card transactions require an investigation orinquiry into whether a transaction may be fraudulent. The problem may befurther compounded if there are multiple possible actions that can betaken with respect to the transactions. Current methods have difficultyin providing an automated or semi-automated mechanism for determiningwhat action if any should be taken for a particular individual or groupsof individuals.

SUMMARY

In accordance with the teachings provided herein, systems and methodsfor operation upon data processing devices are provided for determiningone or more actions to be taken with respect to a first entity. As anexample, a computer-implemented method and system can be configured toreceive data that is related to characteristics of the first entity aswell as data that is related to a plurality of segments. Assignments aredetermined between the first entity and the segments based upon thecharacteristics of the first entity and the characteristics associatedwith the segments. A determined assignment includes a membershipprobability that is indicative of how probable is membership of thefirst entity with respect to a segment. One or more actions aredetermined for the first entity based upon the membership probabilitiesand action information associated with the assigned segments.

As another example, a computer-implemented method and system can beconfigured to receive data that is related to characteristics of thefirst entity as well as data that is related to a plurality of segments.A segment identifies entities having one or more similarcharacteristics. A segment is associated with action information.Assignments are determined between the first entity and the segmentsbased upon the characteristics of the first entity and thecharacteristics associated with the segments. A determined assignmentincludes a membership probability that is indicative of how probable ismembership of the first entity with respect to a segment. One or moreactions are determined for the first entity based upon the membershipprobabilities and the action information associated with the assignedsegments.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram depicting software and computer componentsutilized in determining what action should be taken with respect to anentity.

FIG. 2 is a block diagram depicting an assignment between a segment andan entity.

FIG. 3 is a block diagram depicting assignments being created for anentity based upon the characteristics associated with the entity.

FIG. 4 is a block diagram depicting an example of assignment.

FIG. 5 is a block diagram depicting that the action information of eachof the selected segments can be made part of the assignment.

FIG. 6 is a block diagram depicting use of action information associatedwith selected segments to determine what action should be performed withrespect to the entity.

FIGS. 7 and 8 are flow charts for determining segment assignments.

FIGS. 9A-11B illustrate example data for use in determining segmentassignments.

FIG. 12 is a block diagram depicting a networked environment whereinusers can interact with a clustering system that assists in determiningwhat action should be performed with respect to an entity.

FIG. 13 is a block diagram depicting a stand-alone computer wherein auser can interact with a clustering system that assists in determiningwhat action should be performed with respect to an entity.

DETAILED DESCRIPTION

FIG. 1 depicts at 30 a computer-implemented system for determining oneor more actions 80 to be taken with respect to an entity 40 such as anindividual or organization. An action could include for example whethera credit card company should investigate if fraud may have occurred withrespect to a particular credit card holder. As another example, anaction could include whether to approve a loan application for anindividual. It should also be understood that the system 30 can beconfigured to process one entity or many entities

System 30 determines what action or actions 80 should be performed withrespect to an entity 40. In system 30, process 50 creates assignments 60between segments and the entity 40. Each of the segments is associatedwith action information (e.g., decision information) so that if anassignment 60 is made between the entity 40 and a particular segment,that particular segment's associated action information is also madepart of the assignment. The action information of a selected segment 62is then used by process 70 to determine what action 80 (if any) shouldbe performed with respect to the entity 40.

The types of candidate segments in pool 52 depends upon the situation athand. For example within a credit card transaction processingenvironment, segments can include, among others, a revolver segment anda transactor segment. In this example, a revolver segment includessegment entity members who roll over part of the bill to the next month,instead of paying off the balance in full each month. A transactorsegment includes segment entity members who typically pay off thebalance in full each month. Accordingly, if an entity is assigned to therevolver segment, then the action information (e.g., raise the entity'scredit limit, etc.) associated with the revolver segment is used todetermine what action should be taken for the entity.

It should be understood that many different types entities can beprocessed by the system 30. As an illustration, entities can be anindividual or a collection of individuals (e.g., companies) that areconducting financial transactions. Moreover an entity does not have toconstitute individuals but an entity rather may be representative ofanother aspect of the process for which determination of an action needsto be performed.

FIG. 2 illustrates that an assignment 60 between a segment 62 and anentity 64 includes a membership probability 100. A membershipprobability is indicative of how probable is membership of the entity iswith respect to an assigned segment. In other words, a membershipprobability indicates that there is less than certainty regardingwhether an entity should be part of (e.g., a member) of a segment.

With reference to FIG. 3, process 50 makes assignments 60 for an entity40 based upon the characteristics 110 that are associated with theentity 40. More specifically, the determination of the assignments 60between an entity 40 and the pool of segments 52 is based upon acomparison of the characteristics 110 of the entity 40 with thecharacteristics associated with the candidate segments 52. Thecomparison of characteristics results in generating the value for themembership probability that is associated with an assignment 60. As anillustration, the more similar the characteristics 110 of an entity 40are with respect to characteristics of a particular segment, the higherthe membership probability value 100 will be for the assignment that iscreated between the entity and that particular segment. Conversely, theless similar the characteristics 110 of an entity 40 are with respect tocharacteristics of a particular segment, the lower the membershipprobability value 100 will be for the assignment that is created betweenthe entity and that particular segment. For situations where an entity40 is significantly or entirely dissimilar from a segment, an assignmentdoes not have to be made or the value of the membership probability canbe set relatively low or to a zero value.

FIG. 4 provides an example of assignment 60. In this example, the poolof segments 52 contains “M” number of segments. Associated with eachsegment are entity members that have been previously determined tobelong to a particular segment as well as their respectivecharacteristics. One or more actions are also associated with a segment.In this example, based upon a comparison of the characteristics 110 ofthe entity 40 with the characteristics of each segment, process 50creates the assignments shown at 60.

The assignments 60 include an assignment between the entity and thesecond segment from the pool of segments 52. There is also an assignmentbetween the entity and the fourth segment. By evaluating thecharacteristics 110 of the entity 40 with the segments' associatedcharacteristics, process 50 has determined that the assignment betweenthe second segment and the entity has a membership probability value of0.3. Process 50 has also determined that the assignment between thefourth segment and the entity has a membership probability value of0.65.

FIG. 5 illustrates that the action information of each of the selectedsegments is made part of the assignment. FIG. 6 shows that process 70uses the action information associated with the selected segments fordetermining what action (if any) should be performed with respect to theentity.

As indicated by the higher value in this example, process 50 of FIG. 5had determined that the entity has a greater degree of membership (e.g.,participation) in the fourth segment than it does in the second segment.Accordingly greater weight is accorded by process 70 (of FIG. 6) to theactions associated with the fourth segment than with the second segment.The varying weights can be used to establish a prioritization of actionsto be taken with respect to the entity under analysis.

Process 70 of FIG. 6 indicates the building of action-effect models thathelp assign actions to different entities. A separate action-effectmodel is built for each segment. Entities in a segment are weighted bytheir membership probabilities in the segment under consideration whilebuilding the action-effect model. It should be noted that softclustering provides a means for building better action-effect models byproviding more entities for building the action-effect models. Absentsoft-clustering, other data augmentation techniques such as design ofexperiments would have to be used to build better action-effect models.Process 80 of FIG. 6 indicates the decisions made for each entity usingthe action-effect model.

It should be understood that similar to the other processing flowsdescribed herein, the steps and the order of the steps in this examplemay be altered, modified, removed and/or augmented and still achieve thedesired outcome. As an example, a multiprocessing or multitaskingenvironment can allow two or more steps to be executed concurrently.

A process for determining what segment should be assigned to whichentities can take many forms. For example, the segments can be designedto optimize a predefined utility function, such as such as credit risk,attrition risk, profitability etc. FIG. 7 illustrates where an initialset of segments 220 and a pool of entities 200 are selected in order forprocess 210 to update the assignments between the entities 200 and thecurrent segments 220. The results of the updating of the segments byprocess 210 is a set of new assignments 230 between the entities and thesegments. Process 240 computes the utility function based upon the newassignments 230. If an optimal solution has not been reached asdetermined by process 250, then processing returns to process 210 sothat the procedure can be repeated until there is convergence. When anoptimal solution has been reached as determined by process 250, then theassignments 230 are finalized as segment assignments 260. Segmentassignments 260 can then be used in determining actions for theentities.

FIG. 8 provides an example of additional details that can be used whenupdating the segments via process 210. Process 210 includes in thisexample computing representative characteristics of each segment 300.The computing of representative characteristics may involve finding thestatistical representation of the entities associated with the segment.

Process 310 then assigns actions to each of the segments. Thisassignment at process 310 can include analyzing historical data of asegment to determine which actions were more effective in handlingentities contained within the segment. The more effective actions canthen be assigned to the segment.

It should be understood that assignments can be created in other ways,such as by performing a design of experiments using the characteristicsof the first entity and the characteristics associated with thesegments. An example of using a design of experiments for this purposeincludes identifying the effect of credit line increase on credit risk,profitability etc.

FIGS. 9A-11B provide another example of determining one or more actionsto be taken with respect to an entity. FIGS. 9A-9B depict multipleentities (e.g., credit card accounts) for processing. Associated witheach account are the following raw data items: FICO number (i.e., anumber from a credit scoring model that determines the likelihood ofrepayment), the account's statement balance, merchant balance, creditline, and a specific purchase amount. The raw data also includes accountcycle data at different times: first cycle delinquent amount, secondcycle delinquent amount, etc. Other raw data includes: number ofdelinquent payments in the preceding year, maximum balance last year,late fees paid last year, overlimit fees, and non-sufficient funds (NSF)fee amounts.

FIGS. 10A-10B depict membership probabilities being calculated basedupon the raw data of FIGS. 9A-9B. The membership probabilities includethree segments that are defined in this example as follows: segment 1 isa segment for containing accounts that can be considered situationalrevolvers; segment 2 is a segment for containing accounts that can beconsidered situational transactors; and segment 3 is a segment forcontaining accounts that can be considered situational cash revolvers.

As a description of each of these categories, situational cash revolverscan be defined as a customer who has carried a cash revolving balance atleast 2 consecutive months out of the last 6 months; situationalrevolvers can be defined as a customer who is not a situational cashrevolver but has carried a revolving balance at least 2 consecutivemonths out of the last 6 months; and situational transactors can bedefined as any customer who does not fall into the aforementioned twocategories. For example, segment 1 can contain, to an extent asspecified by a membership probability, accounts having statementbalances greater than $1000, credit limit less than $5000, late feesless than $50, and delinquency amount less than $100.

As an illustration, for the first account (i.e., account number5490098403730050), the membership probability for segment 1 is 0.00since the first account did not share to any significant extent thecharacteristics that are used to describe segment 1. The first accounthas a membership probability of 0.42 for the second segment and has amembership probability of 0.58 for the third segment. These membershipprobabilities show to what extent the first account can be considered amember of a particular segment (e.g., to what extent an account can beconsidered a situational revolver, a situational transactor, and asituational cash revolver) and are determined based upon how well theaccount's characteristics compare to characteristics that define thesegments. Different comparison algorithms can be used to determine towhat extent an account should be clustered with a particular segment,such as a standard k-means clustering method.

Without a membership probability approach, another approach (e.g., ahard segmentation approach) that is used for model building canmisclassify customers that for example have revolved 3 out of the last 6months, but not consecutively, such as every other month. Withmembership probabilities, these customers are likely to have a higherprobability of membership in the “situational revolver” segment andtheir data will be used appropriately. With the hard segmentationapproach, these customers would be classified as “situationaltransactors” and the models are not likely to be as predictive.

The action-effect models can be used to determine what action should betaken with respect to an entity. In this example, an action can be whatproduct (e.g., credit life insurance, magazine, convenience checks, anda free gift) should be offered to the customer holding the account.Probabilities of offer acceptance are determined based upon theaction-effect models. Additional information (e.g., the derived variabledata of FIGS. 10A-10B) can be used in the determining the probabilitiesof acceptance. FIGS. 11A-11B depict examples of the action-effectmodels. Two types of models are built for each segments, the first beingthe probability that the entity will accept the offer, and the secondbeing the revenue generated from this entity if the offer is accepted.The following are example determinations of probabilities of accepting aproduct offer:AE5(i.e., the first account's probability of accepting credit lifeinsurance=0.04=$S5*(0.01+0.06*$W5/100+0.03*$Y5/100+0.03*$AA5/100)+$T5*(0.01+0.03*$W5/100+0.02*$X5/100+0.01*$Y5/100+0.02*$Z5+0.01*$AA5/100+0.02*$AB5)+$U5*(0.01+0.06*$W5/100+0.03*$Y5/100+0.03*$AA5/100)AF5=0.05=S5*(0.02+0.07*W5/100+0.04*Y5/100)+T5*(0.04+0.05*W5/100+0.04*Y5/100+0.03*Z5+0.03*AA5/100)+T5*(0.06+0.04*W5/100+0.05*Y5/100)AG5=0.10=$S5*(0.01+0.03*$Z5/100)+$T5*(0.01+0.01*$W5/100+0.01*$Y5/100+0.01*$Z5+0.03*$AA5/100+0.04*$AB5)+$U5*(0.01+0.07*$W5/100+0.06*$X5+0.06*$Y5/100)AH5=0.11=$S5*(0.01+0.01*$W5/100+0.03*$X5/100+0.03*$AB5/100)+$T5*(0.02+0.02*$W5+0.02*$X5/100+0.04*$AB5)+$U5*(0.04+0.06*$W5/100+0.07*$X5/100+0.03*$AB5/100)

The membership probabilities can also be used to predict revenue:AJ5=(i.e., the first account's predicted revenue with respect to creditlifeinsurance)=$8.73=AE5*($S5*(50+12.6*$Z5/100+14.3*$AB5)+$T5*(66.1+13.4*$Z5/100+23.9*$AB5)+$U5*(81.9+14.7*$Z5/100+34.1*$AB5))AK5=$5.64=AF5*($S5*(20.1+7.6*$Z5/100+4.3*$AB5)+$T5*(26.1+7.4*$Z5/100+12.4*$AB5)+$U5*(31.9+6.4*$Z5/100+24.1*$AB5))AL5=$10.64=AG5*($S5*(30.4+91*$Z5/100+8.3*$AB5)+$T5*(34.1+8.9*$Z5/100+11.4*$AB5)+$U5*(41.3+8.7*$Z5/100+21.6*$AB5))AM5=$5.25=AH5*($S5*(6.1+4.3*$Z5/100+2.1*$AB5)+$T5*(9.3+6.9*$Z5/100+4.6*$AB5)+$U5*(14.6+7.9*$Z5/100+12.5*$AB5))

While examples have been used to disclose the invention, including thebest mode, and also to enable any person skilled in the art to make anduse the invention, the patentable scope of the invention is defined byclaims, and may include other examples that occur to those skilled inthe art. Accordingly the examples disclosed herein are to be considerednon-limiting. As an illustration, the systems and methods may beimplemented on various types of computer architectures, such as forexample on a networked system, on a single general purpose computer,etc.

As an illustration, FIG. 12 depicts a networked environment whereinusers 432 can interact with a clustering system 434. The users 432 caninteract with the clustering system 434 through a number of ways, suchover one or more networks 436.

A server 438 accessible through the network(s) 436 can host theclustering system 434. The same server or different servers can containthe various software instructions 435 (e.g., instructions for creatingsegment assignments, instructions for determining which actions shouldbe taken, etc.) or modules of the clustering system 434. Data store(s)440 can store the data to be analyzed as well as any intermediate orfinal data calculations and data results.

The clustering system 434 can be a web-based analysis and reporting toolthat provides users flexibility and functionality for performing actiondetermination for one or many entities. Moreover, the clustering system434 can be used separately or in conjunction with other softwareprograms, such as with other decision making software techniques.

It should be understood that the clustering system 434 can beimplemented in many different ways, such as on a stand-alone computerfor access by a user as shown in FIG. 13.

It is further noted that the systems and methods may include datasignals conveyed via networks (e.g., local area network, wide areanetwork, interne, combinations thereof, etc.), fiber optic medium,carrier waves, wireless networks, etc. for communication with one ormore data processing devices. The data signals can carry any or all ofthe data disclosed herein that is provided to or from a device.

Additionally, the methods and systems described herein may beimplemented on many different types of processing devices by programcode comprising program instructions that are executable by the deviceprocessing subsystem. The software program instructions may includesource code, object code, machine code, or any other stored data that isoperable to cause a processing system to perform methods describedherein. Other implementations may also be used, however, such asfirmware or even appropriately designed hardware configured to carry outthe methods and systems described herein.

The systems' and methods' data (e.g., associations, mappings, etc.) maybe stored and implemented in one or more different types ofcomputer-implemented ways, such as different types of storage devicesand programming constructs (e.g., data stores, RAM, ROM, Flash memory,flat files, databases, programming data structures, programmingvariables, IF-THEN (or similar type) statement constructs, etc.). It isnoted that data structures describe formats for use in organizing andstoring data in databases, programs, memory, or other computer-readablemedia for use by a computer program.

The systems and methods may be provided on many different types ofcomputer-readable media including computer storage mechanisms (e.g.,CD-ROM, diskette, RAM, flash memory, computer's hard drive, etc.) thatcontain instructions (e.g., software) for use in execution by aprocessor to perform the methods' operations and implement the systemsdescribed herein.

The computer components, software modules, functions, data stores anddata structures described herein may be connected directly or indirectlyto each other in order to allow the flow of data needed for theiroperations. It is also noted that a module or processor includes but isnot limited to a unit of code that performs a software operation, andcan be implemented for example as a subroutine unit of code, or as asoftware function unit of code, or as an object (as in anobject-oriented paradigm), or as an applet, or in a computer scriptlanguage, or as another type of computer code. The software componentsand/or functionality may be located on a single computer or distributedacross multiple computers depending upon the situation at hand.

It should be understood that as used in the description herein andthroughout the claims that follow, the meaning of “a,” “an,” and “the”includes plural reference unless the context clearly dictates otherwise.Also, as used in the description herein and throughout the claims thatfollow, the meaning of “in” includes “in” and “on” unless the contextclearly dictates otherwise. Finally, as used in the description hereinand throughout the claims that follow, the meanings of “and” and “or”include both the conjunctive and disjunctive and may be usedinterchangeably unless the context expressly dictates otherwise; thephrase “exclusive or” may be used to indicate situation where only thedisjunctive meaning may apply.

1. A computer-implemented method for determining one or more actions,comprising: receiving, using one or more processors, data related to oneor more characteristics associated with an entity; receiving, using theone or more processors, data related to a plurality of segments that areassociated with the one or more characteristics, wherein each segment isassociated with action information; determining, using the one or moreprocessors, assignments between the entity and the plurality of segmentsbased upon a comparison of the one or more characteristics associatedwith the entity and the one or more characteristics associated with theplurality of segments, wherein an assignment includes membershipprobabilities indicating the extent to which the entity can beconsidered a member of two or more segments at any point in time,thereby avoiding misclassification caused by a hard-segmentationapproach, and wherein determining the assignments includes: determininga first assignment between the entity and a first segment by calculatinga first membership probability that includes a value indicating acertainty level of entity membership in the first segment, anddetermining a second assignment between the entity and a second segmentby calculating a second membership probability that includes a valueindicating a certainty level of entity membership in the second segment;building, using the one or more processors, an action-effect modelcorresponding to the entity, wherein building includes usingsoft-clustering and weighting the entity according to membershipprobabilities indicating the extent to which the entity can beconsidered a member of each segment in the plurality of segments; andusing the action-effect model to determine, using the one or moreprocessors, one or more actions for the entity based upon the membershipprobabilities associated with the entity and the action informationassociated with each assigned segment.
 2. The method of claim 1, whereinthe entity is an individual that has been determined to be a creditrisk, and wherein the one or more actions are in relation to theindividual.
 3. The method of claim 2, wherein a credit risk predictivemodel is used to determine whether the individual is a credit risk. 4.The method of claim 1, wherein determining the assignments furtherincludes: determining a third assignment between the entity and a thirdsegment by calculating a third membership probability that includes avalue indicating a certainty level of entity membership in the thirdsegment.
 5. The method of claim 1, further comprising: performing adesign of experiments using the one or more characteristics associatedwith the entity and the one or more characteristics associated with theplurality of segments in order to determine the assignments between theentity and the plurality of segments.
 6. The method of claim 1, whereinthe comparison of the characteristics includes determining a degree ofsimilarity between the one or more characteristics associated with theentity and the one or more characteristics associated with the pluralityof segments.
 7. The method of claim 1, wherein the action informationthat is associated with a segment includes one or more actions ordecisions that have been taken with respect to one or more entitiesassociated with the segment.
 8. The method of 1, wherein the assignedsegments are prioritized based on the membership probabilities.
 9. Themethod of claim 1, wherein one of the segments is a revolver segment andanother of the segments is a transactor segment.
 10. The method of claim9, wherein the revolver segment includes segment entity members who rollover part of a bill to a next month; and wherein the transactor segmentincludes segment entity members who pay off a balance in full eachmonth.
 11. The method of claim 1, wherein when the plurality of segmentsoptimize a pre-specified utility function, the method further includes:selecting an initial set of segments from the plurality of segments;updating the assignments between entities and the initial set ofsegments; and computing the utility function based upon the updatedassignments.
 12. The method of claim 11, wherein updating theassignments comprises: computing representative characteristics for eachof the segments in the initial set of segments; and assigning updatedaction information to each of the segments.
 13. The method of claim 1,further comprising: using a membership probability of an assignment todetermine a weight for action information associated with an assignedsegment.
 14. The method of claim 1, wherein the one or more actionsrelate to handling financial transactions.
 15. The method of claim 14,wherein financial transactions include credit card transactions or debitcard transactions of loan application transactions.
 16. Computersoftware stored on one or more non-transitory computer readable mediums,the computer software comprising program code for carrying out a methodfor determining one or more actions, the method comprising: receiving,using one or more processors, data related to one or morecharacteristics associated with an entity receiving data related to aplurality of segments that are associated with the one or morecharacteristics, wherein each segment is associated with actioninformation; determining assignments between the entity and theplurality of segments based upon a comparison of the one or morecharacteristics associated with the entity and the one or morecharacteristics associated with the plurality of segments, wherein anassignment includes membership probabilities indicating the extent towhich the entity can be considered a member of two or more segments atany point in time, thereby avoiding misclassification caused by ahard-segmentation approach, and wherein determining the assignmentsincludes: determining a first assignment between the entity and a firstsegment by calculating a first membership probability that includes avalue indicating a certainty level of entity membership in the firstsegment, and determining a second assignment between the entity and asecond segment by calculating a second membership probability thatincludes a value indicating a certainty level of entity membership inthe second segment; building an action-effect model corresponding to theentity, wherein building includes using soft-clustering and weightingthe entity according to membership probabilities indicating the extentto which the entity can be considered a member of each segment in theplurality of segments; and using the action-effect model to determineone or more actions for the entity based upon the membershipprobabilities associated with the entity and the action informationassociated with each assigned segment.
 17. A system for determining oneor more actions, comprising: a non-transitory computer-readable mediafor storing software instructions; one or more processors coupled to thecomputer-readable media and configured to: receive, using one or moreprocessors, data related to one or more characteristics associated withan entity receive data related to a plurality of segments that areassociated with the one or more characteristics, wherein each segment isassociated with action information; determine assignments between theentity and the plurality of segments based upon a comparison of the oneor more characteristics associated with the entity and the one or morecharacteristics associated with the plurality of segments, wherein anassignment includes membership probabilities indicating the extent towhich the entity can be considered a member of two or more segments atany point in time, thereby avoiding misclassification caused by ahard-segmentation approach, and wherein determining the assignmentsincludes: determining a first assignment between the entity and a firstsegment by calculating a first membership probability that includes avalue indicating a certainty level of entity membership in the firstsegment, and determining a second assignment between the entity and asecond segment by calculating a second membership probability thatincludes a value indicating a certainty level of entity membership inthe second segment; build an action-effect model corresponding to theentity, wherein building includes using soft-clustering and weightingthe entity according to membership probabilities indicating the extentto which the entity can be considered a member of each segment in theplurality of segments; and use the action-effect model to determine oneor more actions for the entity based upon the membership probabilitiesassociated with the entity and the action information associated witheach assigned segment.